File Format Identification System

4. The computer-implemented method of claim 1, wherein the neural network of the embedding model is trained, based on a training set of data, to predict probabilities of occurrences of reference n-gram features in reference data objects in the training set of data.

5. The computer-implemented method of claim 1, wherein the binary data is at least one of a partial file, a corrupted file, a file without file format identification metadata, or an ill-formatted file that is not fully formatted according to a file format specification.

6. The computer-implemented method of claim 1, wherein the file format prediction identifies multiple file formats associated with the binary data.

7. The computer-implemented method of claim 1, wherein the file format prediction indicates one or more confidence levels associated with predictions that the one or more file formats are associated with the binary data.

8. The computer-implemented method of claim 7, wherein the one or more confidence levels are equal to, or greater than, corresponding threshold values associated with the one or more file formats.

12. The computing system of claim 9, wherein the neural network of the embedding model is trained, based on a training set of data, to predict probabilities of occurrences of reference n-gram features in reference data objects in the training set of data.

13. The computing system of claim 9, wherein the binary data is at least one of a partial file, a corrupted file, a file without file format identification metadata, or an ill-formatted file that is not fully formatted according to a file format specification.

14. The computing system of claim 9, wherein the file format prediction indicates one or more confidence levels associated with predictions that the one or more file formats are associated with the binary data.

18. The one or more non-transitory computer-readable media of claim 15, wherein the neural network of the embedding model is trained, based on a training set of data, to predict probabilities of occurrences of reference n-gram features in reference data objects in the training set of data.

19. The one or more non-transitory computer-readable media of claim 15, wherein the binary data is at least one of a partial file, a corrupted file, a file without file format identification metadata, or an ill-formatted file that is not fully formatted according to a file format specification.